下一篇: 神经嵌入用于氨基酸关系的神经嵌入
bioRxiv : the preprint server for biology
|February 3, 2025
概括
NEAR是一种用于蛋白质同质性搜索的新方法,比现有的蛋白质语言模型 (PLM) 提供了更好的速度和准确性. 这种神经网络方法增强了蛋白质数据库的搜索,使其更快,更可靠地识别相关的蛋白质序列.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
背景情况:
- 蛋白质语言模型 (PLM) 在替代蛋白质数据库搜索的传统序列对齐方法方面表现有前途.
- 目前的PLM通常比基于对齐的工具慢,产生更多的假阳性.
- 有效和准确的同质检测对于理解蛋白质功能和进化至关重要.
研究的目的:
- 推出NEAR,一种基于神经网络的方法,旨在提高在大数据库中搜索同类蛋白质的速度和准确性.
- 评估与最先进的PLM和现有的预先过方法对比NEAR的性能.
主要方法:
- NEAR使用了一个ResNet嵌入模型,该模型通过可信的序列对齐指导的对比学习进行训练.
- 它计算了蛋白质序列的每残余嵌入.
- 同类候选物被识别使用一个管道,涉及残留级 k-最近邻居 (k-NN) 搜索和邻居聚合.
主要成果:
- 与最先进的PLM相比,NEAR在远程同类和诱的基准上显示了大幅提高的准确性.
- 该方法显示了更低的内存需求和更快的嵌入和搜索速度.
- 作为配置文件隐藏马尔科夫模型 (pHMM) 搜索的预过器,NEAR至少比HMMER3的预过器快5倍,并且性能优于钉子工具中的预过器.
结论:
- NEAR在蛋白质同质性搜索方面取得了重大进展,提高了速度和准确性.
- 它作为高速预过器的有效性提高了敏感的注释管道,优于当前的方法.
- NEAR为独立同质检测提供了一个有价值的替代方案,其灵敏度可能比标准对齐方法更高.
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